Статті в журналах: "Ocean data"

1

Anonymous. "Ocean data." Eos, Transactions American Geophysical Union 75, no. 42 (1994): 490. http://dx.doi.org/10.1029/eo075i042p00490-04.

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2

Bellingham, James G., and Mike Godin. "Exploring ocean data." ACM SIGMOD Record 37, no. 2 (June 2008): 78–82. http://dx.doi.org/10.1145/1379387.1379410.

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3

Zhuang, Yueting, Yaoguang Wang, Jian Shao, Ling Chen, Weiming Lu, Jianling Sun, Baogang Wei, and Jiangqin Wu. "D-Ocean: an unstructured data management system for data ocean environment." Frontiers of Computer Science 10, no. 2 (October 20, 2015): 353–69. http://dx.doi.org/10.1007/s11704-015-5045-6.

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4

Brown, Murray. "Ocean Data View 4.0." Oceanography 11, no. 2 (1998): 19–21. http://dx.doi.org/10.5670/oceanog.1998.04.

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5

Barisits, Martin, Fernando Barreiro, Thomas Beermann, Karan Bhatia, Kaushik De, Arnaud Dubreuil, Johannes Elmsheuser, et al. "The Data Ocean Project." EPJ Web of Conferences 214 (2019): 04020. http://dx.doi.org/10.1051/epjconf/201921404020.

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Transparent use of commercial cloud resources for scientific experiments is a hard problem. In this article, we describe the first steps of the Data Ocean R&D collaboration between the high-energy physics experiment ATLAS together with Google Cloud Platform, to allow seamless use of Google Compute Engine and Google Cloud Storage for physics analysis. We start by describing the three preliminary use cases that were identified at the beginning of the project. The following sections then detail the work done in the data management system Rucio and the workflow management systems PanDA and Harvester to interface Google Cloud Platform with the ATLAS distributed computing environment, and show the results of the integration tests. Afterwards, we describe the setup and results from a full ATLAS user analysis that was executed natively on Google Cloud Platform, and give estimates on projected costs. We close with a summary and and outlook on future work.

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6

Denner, Warren W., and Christopher N. K. Mooers. "Archived ocean data bases." Eos, Transactions American Geophysical Union 68, no. 45 (1987): 1580. http://dx.doi.org/10.1029/eo068i045p01580-03.

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7

Edwards, Christopher A., Andrew M. Moore, Ibrahim Hoteit, and Bruce D. Cornuelle. "Regional Ocean Data Assimilation." Annual Review of Marine Science 7, no. 1 (January 3, 2015): 21–42. http://dx.doi.org/10.1146/annurev-marine-010814-015821.

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8

Olsen, Are, Robert M. Key, Steven van Heuven, Siv K. Lauvset, Anton Velo, Xiaohua Lin, Carsten Schirnick, et al. "The Global Ocean Data Analysis Project version 2 (GLODAPv2) – an internally consistent data product for the world ocean." Earth System Science Data 8, no. 2 (August 15, 2016): 297–323. http://dx.doi.org/10.5194/essd-8-297-2016.

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Abstract. Version 2 of the Global Ocean Data Analysis Project (GLODAPv2) data product is composed of data from 724 scientific cruises covering the global ocean. It includes data assembled during the previous efforts GLODAPv1.1 (Global Ocean Data Analysis Project version 1.1) in 2004, CARINA (CARbon IN the Atlantic) in 2009/2010, and PACIFICA (PACIFic ocean Interior CArbon) in 2013, as well as data from an additional 168 cruises. Data for 12 core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have been subjected to extensive quality control, including systematic evaluation of bias. The data are available in two formats: (i) as submitted but updated to WOCE exchange format and (ii) as a merged and internally consistent data product. In the latter, adjustments have been applied to remove significant biases, respecting occurrences of any known or likely time trends or variations. Adjustments applied by previous efforts were re-evaluated. Hence, GLODAPv2 is not a simple merging of previous products with some new data added but a unique, internally consistent data product. This compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 6 µmol kg−1 in total alkalinity, 0.005 in pH, and 5 % for the halogenated transient tracers.The original data and their documentation and doi codes are available at the Carbon Dioxide Information Analysis Center (http://cdiac.ornl.gov/oceans/GLODAPv2/). This site also provides access to the calibrated data product, which is provided as a single global file or four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under the doi:10.3334/CDIAC/OTG.NDP093_GLODAPv2. The product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This paper documents the GLODAPv2 methods and products and includes a broad overview of the secondary quality control results. The magnitude of and reasoning behind each adjustment is available on a per-cruise and per-variable basis in the online Adjustment Table.

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9

Adhikary, Subhrangshu, and Saikat Banerjee. "Improved Large-Scale Ocean Wave Dynamics Remote Monitoring Based on Big Data Analytics and Reanalyzed Remote Sensing." Nature Environment and Pollution Technology 22, no. 1 (March 2, 2023): 269–76. http://dx.doi.org/10.46488/nept.2023.v22i01.026.

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Oceans and large water bodies have the potential to generate a large amount of green and renewable energy by harvesting the ocean surface properties like wind waves and tidal waves using Wave Energy Converter (WEC) devices. Although the oceans have this potential, very little ocean energy is harvested because of improper planning and implementation challenges. Besides this, monitoring ocean waves is of immense importance as several ocean-related calamities could be prevented. Also, the ocean serves as the maritime transportation route. Therefore, a need exists for remote and continuous monitoring of ocean waves and preparing strategies for different situations. Remote sensing technology could be utilized for a large scale low-cost opportunity for monitoring entire ocean bodies and extracting several important ocean surface features like wave height, wave time period, and drift velocities that can be used to estimate the ideal locations for power generation and find locations for turbulent waters so that maritime transportation hazards could be prevented. To process this large volume of data, Big Data Analytics techniques have been used to distribute the workload to worker nodes, facilitating a fast calculation of the reanalyzed remote sensing data. The experiment was conducted on Indian Coastline. The findings from the experiment show that a total of 1.86 GWh energy can be harvested from the ocean waves of the Indian Coastline, and locations of turbulent waters can be predicted in real-time to optimize maritime transportation routes.

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10

Peres Teixeira, Carlos Eduardo. "THE DATA WE NEED FOR THE OCEAN WE WANT TO PREDICT: A BRAZILIAN PERSPECTIVE." Arquivos de Ciências do Mar 55, Especial (March 18, 2022): 292–97. http://dx.doi.org/10.32360/acmar.v55iespecial.78513.

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A Predicted Ocean is one of the UN Ocean Decade goals. Ocean observations and numerical simulations of the ocean circulation are at the heart of this outcome. Numerical models are used to understand the present and predict future ocean states, but also the human impact on it, among many other uses. However, its results are only a representation of reality, and we need to validate the numerical model outputs with observational data before using them. Considering its coast extension and the marine economic importance, Brazil does not collect enough physical ocean data and we have only a few real-time observation systems. Unfortunately, due to the COVID and the current national science budget crisis, the number of real-time observations has been further reduced. From a positive perspective, I must believe that this situation will change. We need to be prepared to convince the stakeholders of the importance of observing systems to our society and secure a budget in that regard. This is the way to better predict our oceans. Keywords: ocean modeling, observation systems, Ocean Decade, numerical model validation.

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11

Drakopulos, Lauren, Elizabeth Havice, and Lisa Campbell. "Architecture, agency and ocean data science initiatives: Data-driven transformation of oceans governance." Earth System Governance 12 (April 2022): 100140. http://dx.doi.org/10.1016/j.esg.2022.100140.

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12

Good, Simon A. "Depth Biases in XBT Data Diagnosed Using Bathymetry Data." Journal of Atmospheric and Oceanic Technology 28, no. 2 (February 1, 2011): 287–300. http://dx.doi.org/10.1175/2010jtecho773.1.

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Abstract Expendable bathythermograph (XBT) profiles are an important component of the historical record of subsurface ocean temperature. To correct for time-varying biases in these data, adjustments to XBT depths and/or temperatures have been proposed by a number of groups based on comparisons between XBT data and profiles recorded using other types of instruments. In this study, an alternative method for diagnosing biases has been developed that uses ocean depth information from the XBT profiles and from the General Bathymetry Chart of the Oceans (GEBCO) 30-arc-sec gridded bathymetry. This isolates any depth biases from additional, unrelated temperature biases. Corrections to depths obtained with this method for the Sippican T4 XBT follow a time evolution similar to that found in other studies that derived time-varying adjustments, but are relatively large during the 1980s. Similarities in the evolution of Sippican T7 XBT biases were also observed, but with differences in recent years. Corrections from a study that proposed non-time-varying adjustments were found to broadly remove the biases in the T4 depths but overcorrected the T7 data, with temporal variations in the biases remaining. For the Sippican T10 XBT a more detailed time evolution of depth biases has been obtained than was previously possible. Although corrections have also been derived for approximately 50% of the XBTs for which type and manufacturer are unknown, these should only be used with caution as this study necessarily focuses on shallow water and proportions of different XBT types in use there are not typical of the wider ocean.

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13

Nisumaa, A. M., S. Pesant, R. G. J. Bellerby, B. Delille, J. Middelburg, J. C. Orr, U. Riebesell, T. Tyrrell, D. Wolf-Gladrow, and J. P. Gattuso. "EPOCA/EUR-OCEANS data-mining compilation on the impacts of ocean acidification." Earth System Science Data Discussions 3, no. 1 (March 30, 2010): 109–30. http://dx.doi.org/10.5194/essdd-3-109-2010.

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Abstract. The uptake of anthropogenic CO2 by the oceans has led to a rise in the oceanic partial pressure of CO2, and to a decrease in pH and carbonate ion concentration. This modification of the marine carbonate system is referred to as ocean acidification. Numerous papers report the effects of ocean acidification on marine organisms and communities but few have provided details concerning full carbonate chemistry and complementary observations. Additionally, carbonate system variables are often reported in different units, calculated using different sets of dissociation constants and on different pH scales. Hence the direct comparison of experimental results has been problematic and often misleading. The need was identified to (1) gather data on carbonate chemistry, biological and biogeochemical properties, and other ancillary data from published experimental data, (2) transform the information into common framework, and (3) make data freely available. The present paper is the outcome of an effort to integrate ocean carbonate chemistry data from the literature which has been supported by the European Network of Excellence for Ocean Ecosystems Analysis (EUR-OCEANS) and the European Project on Ocean Acidification (EPOCA). A total of 166 papers were identified, 86 contained enough information to readily compute carbonate chemistry variables, and 67 datasets were archived at PANGAEA – The Publishing Network for Geoscientific & Environmental Data. This data compilation is regularly updated as an ongoing mission of EPOCA. Data access: http://doi.pangaea.de/10.1594/PANGAEA.735138

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14

Anonymous. "Updated ocean data made available to ocean scientific community." Eos, Transactions American Geophysical Union 76, no. 34 (1995): 338. http://dx.doi.org/10.1029/95eo00206.

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15

Moore, J. Keith, Mark R. Abbott, James G. Richman, Walker O. Smith, Timothy J. Cowles, Kenneth H. Coale, Wilford D. Gardner, and Richard T. Barber. "SeaWiFS satellite ocean color data from the Southern Ocean." Geophysical Research Letters 26, no. 10 (May 15, 1999): 1465–68. http://dx.doi.org/10.1029/1999gl900242.

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16

Sturm, Bob L. "Pulse of an Ocean: Sonification of Ocean Buoy Data." Leonardo 38, no. 2 (April 2005): 143–49. http://dx.doi.org/10.1162/0024094053722453.

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The author presents his work in sonifying ocean buoy data for scientific, pedagogical and compositional purposes. Mapping the spectral buoy data to audible frequencies creates interesting and illuminating sonifications of ocean wave dynamics. Several phenomena can be heard, including both those visible and those invisible in graphical representations of the data. The author has worked extensively with this data to compose music and to produce Music from the Ocean, a multi-media CD-ROM demonstrating the data, the phenomena and the sonification. After a brief introduction to physical oceanography, many examples are presented and a composition and installation created from the sonifications are discussed.

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17

Stiles, Bryan W., Marcos Portabella, Xiaofeng Yang, and Gang Zheng. "Editorial for Special Issue “Tropical Cyclones Remote Sensing and Data Assimilation”." Remote Sensing 12, no. 18 (September 19, 2020): 3067. http://dx.doi.org/10.3390/rs12183067.

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Tropical cyclones (TCs) are essential for many reasons, including their destruction of human lives and property and their effect on heat and nutrient fluxes between the ocean’s surface and its depths. A better understanding of ocean fluxes is needed to predict the impact of global climate change on the oceans and to quantify how ocean heat content modulates the dynamics of global climate change. Similarly, improved modeling of nutrient fluxes is crucial for maintaining fisheries and preserving crucial marine ecosystems to benefit both humanity and marine life. Numerous remote sensors measure crucial geophysical quantities before, during, and after TCs, including sea surface temperature (SST), ocean color, chlorophyll concentration, ocean surface winds, sea surface height, and significant wave height. In this special issue, an international group of researchers have written articles describing (1) novel techniques and remote sensors for measuring the aforementioned quantities in tropical cyclones, (2) methods for validating and improving the accuracy of those measurements and harmonizing them among different sensors, (3) scientific analyses that investigate the relationships between remote-sensed ocean surface measurements and in situ measurements of vertical profiles of ocean temperature, salinity, and current, and (4) strategies for utilizing remote-sensed measurements to improve operational forecasts in order to provide better tropical cyclone warnings to human populations.

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18

Gan, Xin Jun, Yong Hua Chen, Yong Ping Xu, Tao Zuo Ni, Jing Bo Jiang, Zhi Tu Deng, and Xiao Long Li. "Design and Development of a Drifting Buoy for Gathering Environmental Data." Applied Mechanics and Materials 651-653 (September 2014): 417–21. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.417.

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Operational meteorologists and Oceanographers rely on real-time environmental data to run their numerical prediction models, even carry on the research. The ground station network is dense and the data of good quality, but there is not enough environmental data from the oceans, particularly in data-sparse areas not covered by commercial ships reporting environmental data. A drifting ocean buoy is described. The drifter consists of three main components: a surface float, a tether assembly and a dimensionally-stable drogue. It utilizes a drag structure which follows the water mass of the ocean as it flows in the form of the ocean current, and which also has an aerodynamically shaped low wind drag mast to minimize wind induced errors in ocean current drift measurements; the drag structure also being stable and resistant to heaving (pitch and roll) so as to maintain a mast carried antenna above the water even at high sea states.

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19

Smirnov, A., B. N. Holben, D. M. Giles, I. Slutsker, N. T. O'Neill, T. F. Eck, A. Macke, et al. "Maritime Aerosol Network as a component of AERONET – first results and comparison with global aerosol models and satellite retrievals." Atmospheric Measurement Techniques Discussions 4, no. 1 (January 8, 2011): 1–32. http://dx.doi.org/10.5194/amtd-4-1-2011.

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Abstract. The Maritime Aerosol Network (MAN) has been collecting data over the oceans since November 2006. Over 80 cruises were completed through early 2010 with deployments continuing. Measurements areas included various parts of the Atlantic Ocean, the Northern and Southern Pacific Ocean, the South Indian Ocean, the Southern Ocean, the Arctic Ocean and inland seas. MAN deploys Microtops hand-held sunphotometers and utilizes a calibration procedure and data processing traceable to AERONET. Data collection included areas that previously had no aerosol optical depth (AOD) coverage at all, particularly vast areas of the Southern Ocean. The MAN data archive provides a valuable resource for aerosol studies in maritime environments. In the current paper we present results of AOD measurements over the oceans, and make a comparison with satellite AOD retrievals and model simulations.

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20

Smirnov, A., B. N. Holben, D. M. Giles, I. Slutsker, N. T. O'Neill, T. F. Eck, A. Macke, et al. "Maritime aerosol network as a component of AERONET – first results and comparison with global aerosol models and satellite retrievals." Atmospheric Measurement Techniques 4, no. 3 (March 21, 2011): 583–97. http://dx.doi.org/10.5194/amt-4-583-2011.

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Abstract. The Maritime Aerosol Network (MAN) has been collecting data over the oceans since November 2006. Over 80 cruises were completed through early 2010 with deployments continuing. Measurement areas included various parts of the Atlantic Ocean, the Northern and Southern Pacific Ocean, the South Indian Ocean, the Southern Ocean, the Arctic Ocean and inland seas. MAN deploys Microtops hand-held sunphotometers and utilizes a calibration procedure and data processing traceable to AERONET. Data collection included areas that previously had no aerosol optical depth (AOD) coverage at all, particularly vast areas of the Southern Ocean. The MAN data archive provides a valuable resource for aerosol studies in maritime environments. In the current paper we present results of AOD measurements over the oceans, and make a comparison with satellite AOD retrievals and model simulations.

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21

Zhang, Shengjia, Hongchun Zhu, Jie Li, Yanrui Yang, and Haiying Liu. "Data-Free Area Detection and Evaluation for Marine Satellite Data Products." Remote Sensing 14, no. 15 (August 8, 2022): 3815. http://dx.doi.org/10.3390/rs14153815.

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The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It can automatically obtain the optimal threshold value for each class of objects. Then, based on ISAM, we perform spectral information mining on first-level Yellow Sea and Bohai Sea data obtained from the Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Color Instrument (OLCI). In this manner, quantitative results of information related to data-free areas of ocean data products are obtained. The findings indicate that the product data of OLCI are optimal with respect to both completeness and continuity. GOCI and MODIS have striking similarities in their quantitative or visualization results for both evaluation metrics. Moreover, a concomitant phenomenon of ocean-covered objects is apparent in the data-free area with temporal and spatial distribution characteristics. The two characteristics are subsequently explored for further analysis. The evaluation method adopted in this study can help to enrich the content of ocean color product evaluation, facilitate the research of cloud detection algorithms and further understand the composition of the data-free regional information of marine data products. The method proposed in this study has a wide application value.

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22

Yan, Changxiang, Jiang Zhu, and Jiping Xie. "An ocean data assimilation system in the Indian Ocean and west Pacific Ocean." Advances in Atmospheric Sciences 32, no. 11 (September 9, 2015): 1460–72. http://dx.doi.org/10.1007/s00376-015-4121-z.

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23

Nisumaa, A. M., S. Pesant, R. G. J. Bellerby, B. Delille, J. J. Middelburg, J. C. Orr, U. Riebesell, T. Tyrrell, D. Wolf-Gladrow, and J. P. Gattuso. "EPOCA/EUR-OCEANS data compilation on the biological and biogeochemical responses to ocean acidification." Earth System Science Data 2, no. 2 (July 8, 2010): 167–75. http://dx.doi.org/10.5194/essd-2-167-2010.

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Abstract. The uptake of anthropogenic CO2 by the oceans has led to a rise in the oceanic partial pressure of CO2, and to a decrease in pH and carbonate ion concentration. This modification of the marine carbonate system is referred to as ocean acidification. Numerous papers report the effects of ocean acidification on marine organisms and communities but few have provided details concerning full carbonate chemistry and complementary observations. Additionally, carbonate system variables are often reported in different units, calculated using different sets of dissociation constants and on different pH scales. Hence the direct comparison of experimental results has been problematic and often misleading. The need was identified to (1) gather data on carbonate chemistry, biological and biogeochemical properties, and other ancillary data from published experimental data, (2) transform the information into common framework, and (3) make data freely available. The present paper is the outcome of an effort to integrate ocean carbonate chemistry data from the literature which has been supported by the European Network of Excellence for Ocean Ecosystems Analysis (EUR-OCEANS) and the European Project on Ocean Acidification (EPOCA). A total of 185 papers were identified, 100 contained enough information to readily compute carbonate chemistry variables, and 81 data sets were archived at PANGAEA – The Publishing Network for Geoscientific & Environmental Data. This data compilation is regularly updated as an ongoing mission of EPOCA. Data access: http://doi.pangaea.de/10.1594/PANGAEA.735138

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24

Jutterström, S., L. G. Anderson, N. R. Bates, R. Bellerby, T. Johannessen, E. P. Jones, R. M. Key, X. Lin, A. Olsen, and A. M. Omar. "Arctic Ocean data in CARINA." Earth System Science Data Discussions 2, no. 1 (August 21, 2009): 281–308. http://dx.doi.org/10.5194/essdd-2-281-2009.

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Abstract. The paper describes the steps taken for quality controlling chosen parameters within the Arctic Ocean data included in the CARINA data set and checking for offsets between the individual cruises. The evaluated parameters are the inorganic carbon parameters (total dissolved inorganic carbon, total alkalinity and pH), oxygen and nutrients: nitrate, phosphate and silicate. More parameters can be found in the CARINA data product, but were not subject to a secondary quality control. The main method in determining offsets between cruises was regional multi-linear regression, after a first rough basin-wide deep-water estimate of each parameter. Lastly, the results of the secondary quality control are discussed as well as suggested adjustments.

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25

Jutterström, S., L. G. Anderson, N. R. Bates, R. Bellerby, T. Johannessen, E. P. Jones, R. M. Key, X. Lin, A. Olsen, and A. M. Omar. "Arctic Ocean data in CARINA." Earth System Science Data 2, no. 1 (February 10, 2010): 71–78. http://dx.doi.org/10.5194/essd-2-71-2010.

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Abstract. The paper describes the steps taken for quality controlling chosen parameters within the Arctic Ocean data included in the CARINA data set and checking for offsets between the individual cruises. The evaluated parameters are the inorganic carbon parameters (total dissolved inorganic carbon, total alkalinity and pH), oxygen and nutrients: nitrate, phosphate and silicate. More parameters can be found in the CARINA data product, but were not subject to a secondary quality control. The main method in determining offsets between cruises was regional multi-linear regression, after a first rough basin-wide deep-water estimate of each parameter. Lastly, the results of the secondary quality control are discussed as well as applied adjustments.

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26

Anderson, D. L. T., J. Sheinbaum, and K. Haines. "Data assimilation in ocean models." Reports on Progress in Physics 59, no. 10 (October 1, 1996): 1209–66. http://dx.doi.org/10.1088/0034-4885/59/10/001.

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27

Bush, S. "Global ocean data program underway." Eos, Transactions American Geophysical Union 73, no. 35 (1992): 370. http://dx.doi.org/10.1029/91eo00282.

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28

Cummings, James A. "Operational multivariate ocean data assimilation." Quarterly Journal of the Royal Meteorological Society 131, no. 613 (October 1, 2005): 3583–604. http://dx.doi.org/10.1256/qj.05.105.

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29

Wunsch, C., and D. Stammer. "III: OCEAN CIRCULATION: Global Ocean Data Assimilation and Geoid Measurements." Space Science Reviews 108, no. 1/2 (2003): 147–62. http://dx.doi.org/10.1023/a:1026298519493.

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30

Boucquey, Noëlle, Kevin St Martin, Luke Fairbanks, Lisa M. Campbell, and Sarah Wise. "Ocean data portals: Performing a new infrastructure for ocean governance." Environment and Planning D: Society and Space 37, no. 3 (January 7, 2019): 484–503. http://dx.doi.org/10.1177/0263775818822829.

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We are currently in what might be termed a “third phase” of ocean enclosures around the world. This phase has involved an unprecedented intensity of map-making that supports an emerging regime of ocean governance where resources are geocoded, multiple and disparate marine uses are weighed against each other, spatial tradeoffs are made, and exclusive rights to spaces and resources are established. The discourse and practice of marine spatial planning inform the contours of this emerging regime. This paper examines the infrastructure of marine spatial planning via two ocean data portals recently created to support marine spatial planning on the East Coast of the United States. Applying theories of ontological politics, critical cartography, and a critical conceptualization of “care,” we examine portal performances in order to link their organization and imaging practices with the ideological and ontological work these infrastructures do, particularly in relation to environmental and human community actors. We further examine how ocean ontologies may be made durable through portal use and repetition, but also how such performances can “slip,” thereby creating openings for enacting marine spatial planning differently. Our analysis reveals how portal infrastructures assemble, edit, and visualize data, and how it matters to the success of particular performances of marine spatial planning.

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31

Pierella Karlusich, Juan José, Federico M. Ibarbalz, and Chris Bowler. "Phytoplankton in the Tara Ocean." Annual Review of Marine Science 12, no. 1 (January 3, 2020): 233–65. http://dx.doi.org/10.1146/annurev-marine-010419-010706.

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Photosynthesis evolved in the ocean more than 2 billion years ago and is now performed by a wide range of evolutionarily distinct organisms, including both prokaryotes and eukaryotes. Our appreciation of their abundance, distributions, and contributions to primary production in the ocean has been increasing since they were first discovered in the seventeenth century and has now been enhanced by data emerging from the Tara Oceans project, which performed a comprehensive worldwide sampling of plankton in the upper layers of the ocean between 2009 and 2013. Largely using recent data from Tara Oceans, here we review the geographic distributions of phytoplankton in the global ocean and their diversity, abundance, and standing stock biomass. We also discuss how omics-based information can be incorporated into studies of photosynthesis in the ocean and show the likely importance of mixotrophs and photosymbionts.

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32

Samuelson, Heidi, and Lauren Gaches. "National Environmental Satellite, Data, and Information Service: Providing Global Observations to Understand Earth Science Systems." Marine Technology Society Journal 49, no. 2 (March 1, 2015): 23–36. http://dx.doi.org/10.4031/mtsj.49.2.19.

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AbstractThe National Oceanic and Atmospheric Administration's (NOAA) mission is to monitor, assess, and predict an ever-changing environment that extends from the bottom of the ocean to the surface of the sun. The National Environmental Satellite, Data, and Information Service (NESDIS) operates a fleet of environmental satellites that provide observations and measurements critical for this assessment of the Earth-sun system, including data for real-time monitoring of severe weather events and for developing numerical prediction models and forecasts. NESDIS also archives the data collected so users can access historical satellite data on atmosphere, land, and oceans dating back to the 1970s and in situ data going back centuries in order to improve our understanding of Earth's climate system. Because the World Ocean is one of the most important drivers of weather and climate on the planet, NOAA satellites provide global data for operational products that affect Earth's ocean ecosystems, including coral bleaching alert areas, detecting harmful algal blooms, and monitoring sea ice. With its high-volume data centers, NESDIS provides access to the world's most comprehensive sources of marine environmental data and information. This paper provides a brief history of NOAA satellites and an overview of the satellites currently in operation. Then, it focuses on satellite data and products that come together to monitor Earth's oceans and provide support for tropical storm monitoring, El Niño Southern Oscillation research, and ocean ecosystem monitoring. The paper concludes with a look toward NOAA's next generation of satellites that will be launching in the coming years and their effect on ocean monitoring.

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33

Wu, Mengmeng, Hui Wang, Liying Wan, Juanjuan Wang, Yi Wang, and Jiuke Wang. "The Impacts of the Application of the Ensemble Optimal Interpolation Method in Global Ocean Wave Data Assimilation." Atmosphere 14, no. 5 (April 30, 2023): 818. http://dx.doi.org/10.3390/atmos14050818.

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The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China’s offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China’s ocean satellite data in ocean wave data assimilation and prediction.

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34

Thomas, Christopher M., Bo Dong, and Keith Haines. "Inverse Modeling of Global and Regional Energy and Water Cycle Fluxes using Earth Observation Data." Journal of Climate 33, no. 5 (March 1, 2020): 1707–23. http://dx.doi.org/10.1175/jcli-d-19-0343.1.

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AbstractThe NASA Energy and Water Cycle Study (NEWS) climatology is a self-consistent coupled annual and seasonal cycle solution for radiative, turbulent, and water fluxes over Earth’s surface using Earth observation data covering 2000–09. Here we seek to improve the NEWS solution, particularly over the ocean basins, by considering spatial covariances in the observation errors (some evidence for which is found by comparing five turbulent flux products over the oceans) and by introducing additional horizontal transports from ocean reanalyses as weak constraints. By explicitly representing large error covariances between surface heat flux components over the major ocean basins we retain the flux contrasts present in the original data and infer additional heat losses over the North Atlantic Ocean, more consistent with a strong Atlantic overturning. This change does not alter the global flux balance but if only the errors in evaporation and precipitation are correlated then those fluxes experience larger adjustments (e.g., the surface latent heat flux increases to 85 ± 2 W m−2). Replacing SeaFlux v1 with J-OFURO v3 (Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations) ocean fluxes also leads to a considerable increase in the global latent heat loss as well as a larger North Atlantic heat loss. Furthermore, including a weak constraint on the horizontal transports of heat and freshwater from high-resolution ocean reanalyses improves the net fluxes over the North Atlantic, Caribbean Sea, and Arctic Ocean, without any impact on the global flux balances. These results suggest that better characterized flux uncertainties can greatly improve the quality of the optimized flux solution.

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35

Rajan, Kanna, Fernando Aguado, Pierre Lermusiaux, João Borges de Sousa, Ajit Subramaniam, and Joaquin Tintore. "METEOR: A Mobile (Portable) ocEan roboTic ObsErvatORy." Marine Technology Society Journal 55, no. 3 (May 1, 2021): 74–75. http://dx.doi.org/10.4031/mtsj.55.3.42.

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Abstract The oceans make this planet habitable and provide a variety of essential ecosystem services ranging from climate regulation through control of greenhouse gases to provisioning about 17% of protein consumed by humans. The oceans are changing as a consequence of human activity but this system is severely under sampled. Traditional methods of studying the oceans, sailing in straight lines, extrapolating a few point measurements have not changed much in 200 years. Despite the tremendous advances in sampling technologies, we often use our autonomous assets the same way. We propose to use the advances in multiplatform, multidisciplinary, and integrated ocean observation, artificial intelligence, marine robotics, new high-resolution coastal ocean data assimilation techniques and computer models to observe and predict the oceans “intelligently”—by deploying self-propelled autonomous sensors and Smallsats guided by data assimilating models to provide observations to reduce model uncertainty in the coastal ocean. This system will be portable and capable of being deployed rapidly in any ocean.

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36

Proctor, R., K. Roberts, and B. J. Ward. "A data delivery system for IMOS, the Australian Integrated Marine Observing System." Advances in Geosciences 28 (September 27, 2010): 11–16. http://dx.doi.org/10.5194/adgeo-28-11-2010.

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Abstract. The Integrated Marine Observing System (IMOS, www.imos.org.au), an AUD $150 m 7-year project (2007–2013), is a distributed set of equipment and data-information services which, among many applications, collectively contribute to meeting the needs of marine climate research in Australia. The observing system provides data in the open oceans around Australia out to a few thousand kilometres as well as the coastal oceans through 11 facilities which effectively observe and measure the 4-dimensional ocean variability, and the physical and biological response of coastal and shelf seas around Australia. Through a national science rationale IMOS is organized as five regional nodes (Western Australia – WAIMOS, South Australian – SAIMOS, Tasmania – TASIMOS, New SouthWales – NSWIMOS and Queensland – QIMOS) surrounded by an oceanic node (Blue Water and Climate). Operationally IMOS is organized as 11 facilities (Argo Australia, Ships of Opportunity, Southern Ocean Automated Time Series Observations, Australian National Facility for Ocean Gliders, Autonomous Underwater Vehicle Facility, Australian National Mooring Network, Australian Coastal Ocean Radar Network, Australian Acoustic Tagging and Monitoring System, Facility for Automated Intelligent Monitoring of Marine Systems, eMarine Information Infrastructure and Satellite Remote Sensing) delivering data. IMOS data is freely available to the public. The data, a combination of near real-time and delayed mode, are made available to researchers through the electronic Marine Information Infrastructure (eMII). eMII utilises the Australian Academic Research Network (AARNET) to support a distributed database on OPeNDAP/THREDDS servers hosted by regional computing centres. IMOS instruments are described through the OGC Specification SensorML and where-ever possible data is in CF compliant netCDF format. Metadata, conforming to standard ISO 19115, is automatically harvested from the netCDF files and the metadata records catalogued in the OGC GeoNetwork Metadata Entry and Search Tool (MEST). Data discovery, access and download occur via web services through the IMOS Ocean Portal (http://imos.aodn.org.au) and tools for the display and integration of near real-time data are in development.

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37

Jiang, Fan, Jitong Ma, Baosen Wang, Feifei Shen, and Lingling Yuan. "Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network." Security and Communication Networks 2021 (October 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/5665386.

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With the rapid development of maritime technologies, a huge amount of ocean data has been acquired through the state-of-the-art ocean equipment to get better understanding and development of ocean. The prediction and correction of oceanic observation data play a fundamental and important role in the oceanic relevant applications, including both civilian and military fields. On the basis of Argo data, aiming at predicting and correcting the oceanic observation data, we propose an ocean temperature and salinity prediction approach in this paper. In our approach, firstly, bounded nonlinear function is utilized for dataset quality control, which can effectively eliminate the influence of spikes or outliers in Argo data. Then, RBF neural network is used for high-resolution Argo dataset construction. Finally, a bidirectional LSTM framework is proposed to predict and analyze the ocean temperature and salinity on the basis of BOA Argo data. Experimental results demonstrate that the proposed bidirectional LSTM framework can accurately predict the ocean temperature and salinity and enable outstanding performance in oceanic observation data prediction and correction. The proposed approach is also important for the realization of Argo dataset automatic quality control.

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38

Zweng, Melissa M., Tim P. Boyer, Olga K. Baranova, James R. Reagan, Dan Seidov, and Igor V. Smolyar. "An inventory of Arctic Ocean data in the World Ocean Database." Earth System Science Data 10, no. 1 (March 29, 2018): 677–87. http://dx.doi.org/10.5194/essd-10-677-2018.

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Abstract. The World Ocean Database (WOD) contains over 1.3 million oceanographic casts (where cast refers to an oceanographic profile or set of profiles collected concurrently at more than one depth between the ocean surface and ocean bottom) collected in the Arctic Ocean basin and its surrounding marginal seas. The data, collected from 1849 to the present, come from many submitters and countries, and were collected using a variety of instruments and platforms. These data, along with the derived products World Ocean Atlas (WOA) and the Arctic Regional Climatologies, are exceptionally useful – the data are presented in a standardized, easy to use format and include metadata and quality control information. Collecting data in the Arctic Ocean is challenging, and coverage in space and time ranges from excellent to nearly non-existent. WOD continues to compile a comprehensive collection of Arctic Ocean profile data, ideal for oceanographic, environmental and climatic analyses (https://doi.org/10.7289/V54Q7S16).

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39

Carton, James A., and Benjamin S. Giese. "A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA)." Monthly Weather Review 136, no. 8 (August 1, 2008): 2999–3017. http://dx.doi.org/10.1175/2007mwr1978.1.

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Abstract This paper describes the Simple Ocean Data Assimilation (SODA) reanalysis of ocean climate variability. In the assimilation, a model forecast produced by an ocean general circulation model with an average resolution of 0.25° × 0.4° × 40 levels is continuously corrected by contemporaneous observations with corrections estimated every 10 days. The basic reanalysis, SODA 1.4.2, spans the 44-yr period from 1958 to 2001, which complements the span of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA-40). The observation set for this experiment includes the historical archive of hydrographic profiles supplemented by ship intake measurements, moored hydrographic observations, and remotely sensed SST. A parallel run, SODA 1.4.0, is forced with identical surface boundary conditions, but without data assimilation. The new reanalysis represents a significant improvement over a previously published version of the SODA algorithm. In particular, eddy kinetic energy and sea level variability are much larger than in previous versions and are more similar to estimates from independent observations. One issue addressed in this paper is the relative importance of the model forecast versus the observations for the analysis. The results show that at near-annual frequencies the forecast model has a strong influence, whereas at decadal frequencies the observations become increasingly dominant in the analysis. As a consequence, interannual variability in SODA 1.4.2 closely resembles interannual variability in SODA 1.4.0. However, decadal anomalies of the 0–700-m heat content from SODA 1.4.2 more closely resemble heat content anomalies based on observations.

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40

Velo, A., F. F. Pérez, X. Lin, R. M. Key, T. Tanhua, M. de la Paz, A. Olsen, S. van Heuven, S. Jutterström, and A. F. Ríos. "CARINA data synthesis project: pH data scale unification and cruise adjustments." Earth System Science Data 2, no. 1 (May 11, 2010): 133–55. http://dx.doi.org/10.5194/essd-2-133-2010.

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Abstract. Data on carbon and carbon-relevant hydrographic and hydrochemical parameters from 188 previously non-publicly available cruise data sets in the Artic Mediterranean Seas (AMS), Atlantic Ocean and Southern Ocean have been retrieved and merged to a new database: CARINA (CARbon IN the Atlantic Ocean). These data have gone through rigorous quality control (QC) procedures to assure the highest possible quality and consistency. The data for most of the measured parameters in the CARINA database were objectively examined in order to quantify systematic differences in the reported values. Systematic biases found in the data have been corrected in the data products, three merged data files with measured, calculated and interpolated data for each of the three CARINA regions; AMS, Atlantic Ocean and Southern Ocean. Out of a total of 188 cruise entries in the CARINA database, 59 reported pH measured values. All reported pH data have been unified to the Sea-Water Scale (SWS) at 25 °C. Here we present details of the secondary QC of pH in the CARINA database and the scale unification to SWS at 25 °C. The pH scale has been converted for 36 cruises. Procedures of quality control, including crossover analysis between cruises and inversion analysis are described. Adjustments were applied to the pH values for 21 of the cruises in the CARINA dataset. With these adjustments the CARINA database is consistent both internally as well as with the GLODAP data, an oceanographic data set based on the World Hydrographic Program in the 1990s. Based on our analysis we estimate the internal consistency of the CARINA pH data to be 0.005 pH units. The CARINA data are now suitable for accurate assessments of, for example, oceanic carbon inventories and uptake rates, for ocean acidification assessment and for model validation.

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41

Wu, Qiaoyan, and Yilei Wang. "Comparison of Oceanic Multisatellite Precipitation Data from Tropical Rainfall Measurement Mission and Global Precipitation Measurement Mission Datasets with Rain Gauge Data from Ocean Buoys." Journal of Atmospheric and Oceanic Technology 36, no. 5 (May 2019): 903–20. http://dx.doi.org/10.1175/jtech-d-18-0152.1.

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AbstractThree satellite-derived precipitation datasets [the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) dataset, the NOAA Climate Prediction Center morphing technique (CMORPH) dataset, and the newly available Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) dataset] are compared with data obtained from 55 rain gauges mounted on floating buoys in the tropics for the period 1 April 2014–30 April 2017. All three satellite datasets underestimate low rainfall and overestimate high rainfall in the tropical Pacific Ocean, but the TMPA dataset does this the most. In the high-rainfall (higher than 4 mm day−1) Atlantic region, all three satellite datasets overestimate low rainfall and underestimate high rainfall, but the IMERG dataset does this the most. For the Indian Ocean, all three rainfall satellite datasets overestimate rainfall at some gauges and underestimate it at others. Of these three satellite products, IMERG is the most accurate in estimating mean precipitation over the tropical Pacific and Indian Oceans, but it is less accurate over the tropical Atlantic Ocean for regions of high rainfall. The differences between the three satellite datasets vary by region and there is a need to consider uncertainties in the data before using them for research.

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42

Winch, D. E., and S. K. Runcorn. "Geomagnetic Observatory Data and Ocean Circulation." Exploration Geophysics 24, no. 2 (June 1993): 139–43. http://dx.doi.org/10.1071/eg993139.

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43

Fox, Daniel, Charlie Barron, Michael Carnes, Martin Booda, Germana Peggion, and John Van Gurley. "The Modular Ocean Data Assimilation System." Oceanography 15, no. 1 (2002): 22–28. http://dx.doi.org/10.5670/oceanog.2002.33.

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44

Cummings, James, Laurent Bertino, Pierre Brasseur, Ichiro Fukumori, Masafumi Kamachi, Matthew Martin, Kristian Mogensen, et al. "Ocean Data Assimilation Systems for GODAE." Oceanography 22, no. 3 (September 1, 2009): 96–109. http://dx.doi.org/10.5670/oceanog.2009.69.

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45

Balcerak, Ernie. "Reconstructing ocean properties from seismic data." Eos, Transactions American Geophysical Union 93, no. 4 (January 24, 2012): 48. http://dx.doi.org/10.1029/2012eo040013.

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46

Lavender, S., O. Fanton d'Andon, S. Kay, L. Bourg, S. Emsley, Nicolas Gilles, T. Nightingale, et al. "Applying uncertainties to ocean colour data." Metrologia 49, no. 2 (March 2, 2012): S17—S20. http://dx.doi.org/10.1088/0026-1394/49/2/s17.

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47

Chapman, Piers, and Worth D. Nowlin. "Ocean data synthesis offers research opportunities." Eos, Transactions American Geophysical Union 81, no. 10 (March 7, 2000): 102–7. http://dx.doi.org/10.1029/00eo00065.

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48

McMahon, Clive R., and Robert Harcourt. "Seals collect more Southern Ocean data." Nature 513, no. 7516 (September 2014): 33. http://dx.doi.org/10.1038/513033e.

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49

Parris, Thomas M. "Bytes of Note: Surfing Ocean Data." Environment: Science and Policy for Sustainable Development 38, no. 7 (September 1996): 45. http://dx.doi.org/10.1080/00139157.1996.9930985.

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50

Casasanta, Lorenzo, and Samuel H. Gray. "PS imaging of ocean-bottom data." Leading Edge 34, no. 4 (April 2015): 414–20. http://dx.doi.org/10.1190/tle34040414.1.

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